χ‐sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation

ABSTRACT Magnetic susceptibility source separation (χ‐separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill‐conditioned problem of dipole inversion, suffering from so‐called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation (R2′=R2*−R2$$ {R}_2^{\prime }={R}_2^{\ast }-{R}_2 $$) to complement frequency shift information for estimating susceptibility source concentrations, requiring time‐consuming data acquisition for R2$$ {R}_2 $$ (e.g., multi‐echo spin‐echo) in addition to multi‐echo GRE data for R2*$$ {R}_2^{\ast } $$. To address these challenges, we develop a new deep learning network, χ‐sepnet, and propose two deep learning‐based susceptibility source separation pipelines, χ‐sepnet‐R2′$$ {R}_2^{\prime } $$ for inputs with multi‐echo GRE and multi‐echo spin‐echo (or turbo spin‐echo) and χ‐sepnet‐R2*$$ {R}_2^{\ast } $$ for input with multi‐echo GRE only. The neural network is trained using multiple head orientation data that provide streaking artifact‐free labels, generating high‐quality χ‐separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source‐separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from the conventional regularization‐based reconstruction methods. In quantitative analysis, χ‐sepnet‐R2′$$ {R}_2^{\prime } $$ achieves the best outcomes followed by χ‐sepnet‐R2*$$ {R}_2^{\ast } $$, outperforming the conventional methods. When the lesions of multiple sclerosis patients are classified into subtypes, most lesions are identified as the same subtype in the maps from χ‐sepnet‐R2′$$ {R}_2^{\prime } $$ and χ‐sepnet‐R2*$$ {R}_2^{\ast } $$ (paramagnetic susceptibility: 99.6% and diamagnetic susceptibility: 98.4%; both out of 250 lesions). The χ‐sepnet‐R2*$$ {R}_2^{\ast } $$ pipeline, which only requires multi‐echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary. We proposed two deep learning‐powered susceptibility source separation pipelines: χ‐sepnet‐R2′$